Queries · q346 · VP Platform Engineering · week 2, night 4
“Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1; shared with Samuel Whitcombe, Head of Merchant Success
Leadership wants a review of platform reliability in 2025: failed and slow background jobs, health check failures, rate limiting, webhook delivery problems, and which merchants felt them.
The manager, at the start of week 2
Subject: Re: 2025 Platform Reliability Review — good start, a few things I need tightened up
Lucas,
Thanks — this is useful and more honest than I expected on the limits of what we can see. A few things stood out and need more work before Friday's memo goes to leadership.
First, the retention gap is itself a finding, not just a caveat. We're flying with 90 days of job history and 60 days of health-check history — that means we genuinely don't know how 2025 looked before October. I want that stated plainly and separately in the memo as a reliability/observability gap in its own right, not buried as a footnote under the carts.remind story.
Second, the ERP/WMS/Accounting webhook stuck-retry pattern is, to me, the most important and actionable thing in here — more than the one-week carts.remind incident. I want that properly validated and sized, not eyeballed, before we say anything about it to leadership.
Questions for this week:
On rate limiting: agreed, drop it for this cycle — don't spend a slot trying to force an answer out of config data alone. Just flag it as a 2026 instrumentation ask, as you already have.
Priority for your remaining slots: #1 and #2 above. The webhook/sync issue is the one I can take into a fix-it conversation with engineering next week if the numbers hold up — I'd rather have that nailed down than more polish on the carts.remind narrative.
Diego
The analyst · week 2, night 4, request 3 of 3
Report 1 confirms health checks for checkout/storefront run only every 2 hours (checks exist at 12:00, 14:00, 16:00 but nothing in between) - so even though monitoring did catch a 100%-unhealthy reading at 14:00, our detection granularity for this path is coarse (up to 2 hours to notice), which is itself worth flagging for Diego's #3. Report 2 is the smoking gun on business impact: hour 14 had just 1 order and hour 15 has NO row at all (zero orders) on Black Friday, versus 7-11 orders in every neighboring hour that day - a near-total order freeze for roughly 2 hours on the year's biggest shopping day. Report 3 shows Black Friday 2025 total revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite the business having grown since then, which is directionally consistent with real lost sales, though other factors could contribute and I haven't isolated the outage's exact share of that gap. This is my last night before the memo, so I'm using the 3 slots to: (1) precisely size the lost-order/revenue window by comparing the same 14:00-16:00 UTC slot across Nov 28 against two baseline Fridays and Black Friday 2024, so I can give Diego an actual number rather than an impression; (2) check whether checkout/storefront ever showed another severe (>=50% unhealthy) outage anywhere else in the Nov-Dec window we have, to answer whether this was a true one-off or a recurring latent risk; (3) check payment attempts during the same window, since a checkout-path outage should show up there too and would confirm this was a full checkout failure, not just a reminder-email glitch.
The engineer · 1 dry run on the empty schema, then the submission
SELECT EXTRACT(HOUR FROM pa.attempted_at) AS hour, COUNT(*) AS total_payment_attempts, COUNT(*) FILTER (WHERE pa.status IN ('authorized', 'captured')) AS captured_or_authorized_count, COUNT(*) FILTER (WHERE pa.status = 'failed') AS failed_count, ROUND( COUNT(*) FILTER (WHERE pa.status = 'failed')::decimal / NULLIF(COUNT(*), 0) * 100, 2 ) AS failure_rate_pct FROM payment_attempts pa WHERE pa.attempted_at >= DATE '2025-11-28' AND pa.attempted_at < DATE '2025-11-29' GROUP BY EXTRACT(HOUR FROM pa.attempted_at) ORDER BY hour ASC
“Query compiles cleanly and matches the requested grain/columns.”
“Submitted. The query buckets payment_attempts by UTC hour for 2025-11-28, computing total attempts, captured/authorized counts, failed counts, and failure rate — across all tenants, since the outage question concerns the platform's checkout path as a whole rather than any single merchant. Any hour with zero attempts will simply not appear in the printed rows, as anticipated in the request.”
As Lucas Moreau saw it, from the small dataset the agents worked on
hour | total_payment_attempts | captured_or_authorized_count | failed_count | failure_rate_pct -----+------------------------+------------------------------+--------------+----------------- 0 | 4 | 4 | 0 | 0.0 1 | 5 | 5 | 0 | 0.0 2 | 2 | 2 | 0 | 0.0 3 | 2 | 1 | 1 | 50.0 4 | 4 | 4 | 0 | 0.0 5 | 2 | 2 | 0 | 0.0 6 | 6 | 5 | 1 | 16.67 7 | 4 | 3 | 1 | 25.0 8 | 9 | 8 | 1 | 11.11 9 | 13 | 12 | 1 | 7.69 10 | 3 | 3 | 0 | 0.0 11 | 7 | 7 | 0 | 0.0 12 | 15 | 15 | 0 | 0.0 13 | 14 | 14 | 0 | 0.0 14 | 3 | 3 | 0 | 0.0 15 | 2 | 2 | 0 | 0.0 16 | 17 | 13 | 4 | 23.53 17 | 11 | 11 | 0 | 0.0 18 | 9 | 8 | 1 | 11.11 19 | 18 | 17 | 1 | 5.56 20 | 17 | 17 | 0 | 0.0 21 | 18 | 18 | 0 | 0.0 22 | 13 | 12 | 1 | 7.69 23 | 6 | 6 | 0 | 0.0 (24 rows)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
SELECT EXTRACT(HOUR FROM pa.attempted_at) AS hour, COUNT(*) AS total_payment_attempts, COUNT(*) FILTER (WHERE pa.status IN ('authorized', 'captured')) AS captured_or_authorized_count, COUNT(*) FILTER (WHERE pa.status = 'failed') AS failed_count, ROUND( COUNT(*) FILTER (WHERE pa.status = 'failed')::decimal / NULLIF(COUNT(*), 0) * 100, 2 ) AS failure_rate_pct CAST(COUNT(*) FILTER (WHERE pa.status = 'failed') AS decimal(18,6)) / NULLIF(COUNT(*), 0) * 100 AS failure_rate_pctFROM payment_attempts paWHERE pa.attempted_at >= DATE '2025-11-28' AND pa.attempted_at < DATE '2025-11-29'GROUP BY EXTRACT(HOUR FROM pa.attempted_at)ORDER BY hour ASCORDER BY hour ASC NULLS LAST
SELECT EXTRACT(HOUR FROM pa.attempted_at) AS hour, COUNT(*) AS total_payment_attempts, COUNT(*) FILTER (WHERE pa.status IN ('authorized', 'captured')) AS captured_or_authorized_count, COUNT(*) FILTER (WHERE pa.status = 'failed') AS failed_count, CAST(COUNT(*) FILTER (WHERE pa.status = 'failed') AS decimal(18,6)) / NULLIF(COUNT(*), 0) * 100 AS failure_rate_pct FROM payment_attempts pa WHERE pa.attempted_at >= DATE '2025-11-28' AND pa.attempted_at < DATE '2025-11-29' GROUP BY EXTRACT(HOUR FROM pa.attempted_at) ORDER BY hour ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Lucas Moreau (analyst for the VP Platform Engineering):
Title: Payment Attempts During the Nov 28 2025 Outage Window
Question: Did payment processing itself fail or stall during the same 14:00-16:00 UTC window, confirming a full checkout-path outage rather than just the reminder-email job?
Grain: one row per hour of the day, for 2025-11-28 (UTC)
Columns:
- hour
- total_payment_attempts
- captured_or_authorized_count
- failed_count
- failure_rate_pct
Details: Table payment_attempts, filtered to attempted_at on 2025-11-28 (all 24 hours UTC). Group by hour of attempted_at. captured_or_authorized_count = count where status in ('authorized','captured'); failed_count = count where status='failed'; failure_rate_pct = failed_count / total_payment_attempts * 100, rounded 2dp. Order by hour ascending (0-23); hours with zero attempts simply won't appear, note the gap if any in the report.
Check your query with the warehouse's dry_run tool, then hand it in with submit.dry_runSELECT EXTRACT(HOUR FROM pa.attempted_at) AS hour, COUNT(*) AS total_payment_attempts, COUNT(*) FILTER (WHERE pa.status IN ('authorized', 'captured')) AS captured_or_authorized_count, COUNT(*) FILTER (WHERE pa.status = 'failed') AS failed_count, ROUND( COUNT(*) FILTER (WHERE pa.status = 'failed')::decimal / NULLIF(COUNT(*), 0) * 100, 2 ) AS failure_rate_pct FROM payment_attempts pa WHERE pa.attempted_at >= DATE '2025-11-28' AND pa.attempted_at < DATE '2025-11-29' GROUP BY EXTRACT(HOUR FROM pa.attempted_at) ORDER BY hour ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: hour, total_payment_attempts, captured_or_authorized_count, failed_count, failure_rate_pct
submitSELECT EXTRACT(HOUR FROM pa.attempted_at) AS hour, COUNT(*) AS total_payment_attempts, COUNT(*) FILTER (WHERE pa.status IN ('authorized', 'captured')) AS captured_or_authorized_count, COUNT(*) FILTER (WHERE pa.status = 'failed') AS failed_count, ROUND( COUNT(*) FILTER (WHERE pa.status = 'failed')::decimal / NULLIF(COUNT(*), 0) * 100, 2 ) AS failure_rate_pct FROM payment_attempts pa WHERE pa.attempted_at >= DATE '2025-11-28' AND pa.attempted_at < DATE '2025-11-29' GROUP BY EXTRACT(HOUR FROM pa.attempted_at) ORDER BY hour ASC
Submitted to tonight's batch. Your work on this request is done.